v2 Cell Type Counting
D. Ford Hannum Jr.
8/27/2020
knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scRNAseq_2.2.0 limma_3.44.3
## [3] tidyr_1.1.1 dplyr_1.0.2
## [5] SingleR_1.2.4 MAST_1.14.0
## [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
## [9] DelayedArray_0.14.1 matrixStats_0.56.0
## [11] Biobase_2.48.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] data.table_1.13.0 ggplot2_3.3.2
## [19] Seurat_3.2.0
##
## loaded via a namespace (and not attached):
## [1] AnnotationHub_2.20.1 BiocFileCache_1.12.1
## [3] plyr_1.8.6 igraph_1.2.5
## [5] lazyeval_0.2.2 splines_4.0.2
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] digest_0.6.25 htmltools_0.5.0
## [11] magrittr_1.5 memoise_1.1.0
## [13] tensor_1.5 cluster_2.1.0
## [15] ROCR_1.0-11 globals_0.12.5
## [17] colorspace_1.4-1 blob_1.2.1
## [19] rappdirs_0.3.1 ggrepel_0.8.2
## [21] xfun_0.16 crayon_1.3.4
## [23] RCurl_1.98-1.2 jsonlite_1.7.0
## [25] spatstat_1.64-1 spatstat.data_1.4-3
## [27] survival_3.2-3 zoo_1.8-8
## [29] ape_5.4-1 glue_1.4.1
## [31] polyclip_1.10-0 gtable_0.3.0
## [33] zlibbioc_1.34.0 XVector_0.28.0
## [35] leiden_0.3.3 BiocSingular_1.4.0
## [37] future.apply_1.6.0 abind_1.4-5
## [39] scales_1.1.1 DBI_1.1.0
## [41] miniUI_0.1.1.1 Rcpp_1.0.5
## [43] viridisLite_0.3.0 xtable_1.8-4
## [45] reticulate_1.16 bit_4.0.4
## [47] rsvd_1.0.3 htmlwidgets_1.5.1
## [49] httr_1.4.2 RColorBrewer_1.1-2
## [51] ellipsis_0.3.1 ica_1.0-2
## [53] pkgconfig_2.0.3 uwot_0.1.8
## [55] dbplyr_1.4.4 deldir_0.1-28
## [57] tidyselect_1.1.0 rlang_0.4.7
## [59] reshape2_1.4.4 later_1.1.0.1
## [61] AnnotationDbi_1.50.3 munsell_0.5.0
## [63] BiocVersion_3.11.1 tools_4.0.2
## [65] generics_0.0.2 RSQLite_2.2.0
## [67] ExperimentHub_1.14.1 ggridges_0.5.2
## [69] evaluate_0.14 stringr_1.4.0
## [71] fastmap_1.0.1 yaml_2.2.1
## [73] goftest_1.2-2 knitr_1.29
## [75] bit64_4.0.2 fitdistrplus_1.1-1
## [77] purrr_0.3.4 RANN_2.6.1
## [79] pbapply_1.4-3 future_1.18.0
## [81] nlme_3.1-148 mime_0.9
## [83] compiler_4.0.2 plotly_4.9.2.1
## [85] curl_4.3 png_0.1-7
## [87] interactiveDisplayBase_1.26.3 spatstat.utils_1.17-0
## [89] tibble_3.0.3 stringi_1.4.6
## [91] lattice_0.20-41 Matrix_1.2-18
## [93] vctrs_0.3.2 pillar_1.4.6
## [95] lifecycle_0.2.0 BiocManager_1.30.10
## [97] lmtest_0.9-37 RcppAnnoy_0.0.16
## [99] BiocNeighbors_1.6.0 cowplot_1.0.0
## [101] bitops_1.0-6 irlba_2.3.3
## [103] httpuv_1.5.4 patchwork_1.0.1
## [105] R6_2.4.1 promises_1.1.1
## [107] KernSmooth_2.23-17 gridExtra_2.3
## [109] codetools_0.2-16 MASS_7.3-52
## [111] assertthat_0.2.1 withr_2.2.0
## [113] sctransform_0.2.1 GenomeInfoDbData_1.2.3
## [115] mgcv_1.8-31 grid_4.0.2
## [117] rpart_4.1-15 rmarkdown_2.3
## [119] DelayedMatrixStats_1.10.1 Rtsne_0.15
## [121] shiny_1.5.0## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl
## 653 1315 1037 2144 2608
## Nbeal_cntrl
## 911
##
## Enriched Not enriched
## 3005 5663
##
## Control Migr1 Mpl
## 1948 2797 3923
Introduction
In v2 of the analysis we decided to include the control mice from the Nbeal experiment with the Migr1 and Mpl mice. The thought is that it may be good to have another control, since the Migr1 control has irradiated and had a bone marrow transplantation. I’m going to split the Rmarkdown files into separate part, to better organize my analysis.
This File
I’m going to go with the consensus names from the labeling stage and produce figures covering the distribution of cell types within clusters, conditions (enriched/not enriched), experiments (Mpl, Migr, Nbeal_cnt), states(condition + experiment), etc.
UMAP Projections
UMAP projections of the data of different subsets of the data with the cell type labels.
## B-cell T cell Macrophage Granulocyte MEP/MCP
## 1464 203 351 4613 592
## CMP Monocyte Erythrocyte Megakarycoyte HSPC
## 355 600 312 120 58##
## B-cell CMP Erythrocyte Granulocyte HSPC
## 1464 355 312 4613 58
## Macrophage Megakarycoyte MEP/MCP Monocyte T cell
## 351 120 592 600 203
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl
## 653 1315 1037 2144 2608
## Nbeal_cntrl
## 911##
## B-cell CMP Erythrocyte Granulocyte HSPC Macrophage
## enrMigr1 211 10 65 162 0 129
## enrMpl 7 78 26 487 5 85
## enrNbeal_cntrl 307 8 29 460 44 28
## Migr1 775 82 76 815 3 69
## Mpl 15 159 110 2095 0 35
## Nbeal_cntrl 149 18 6 594 6 5
##
## Megakarycoyte MEP/MCP Monocyte T cell
## enrMigr1 8 12 42 14
## enrMpl 20 500 106 1
## enrNbeal_cntrl 77 10 55 19
## Migr1 3 11 179 131
## Mpl 8 51 125 10
## Nbeal_cntrl 4 8 93 28
##
## Enriched Not enriched
## 3005 5663##
## B-cell CMP Erythrocyte Granulocyte HSPC Macrophage
## Enriched 525 96 120 1109 49 242
## Not enriched 939 259 192 3504 9 109
##
## Megakarycoyte MEP/MCP Monocyte T cell
## Enriched 105 522 203 34
## Not enriched 15 70 397 169
##
## Control Migr1 Mpl
## 1948 2797 3923##
## B-cell CMP Erythrocyte Granulocyte HSPC Macrophage Megakarycoyte
## Control 456 26 35 1054 50 33 81
## Migr1 986 92 141 977 3 198 11
## Mpl 22 237 136 2582 5 120 28
##
## MEP/MCP Monocyte T cell
## Control 18 148 47
## Migr1 23 221 145
## Mpl 551 231 11
Quantification (Bar graphs & Tables/Heatmaps)
Quantification for each cluster
## [1] "B-cell"
## [1] "T cell"
## [1] "Macrophage"
## [1] "Granulocyte"
## [1] "MEP/MCP"
## [1] "CMP"
## [1] "Monocyte"
## [1] "Erythrocyte"
## [1] "Megakarycoyte"
## [1] "HSPC"
